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Multiscale modeling through MUSIC multi-simulation: Modeling a dendritic spine using MOOSE and NeuroRD
KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC), Numerical Analysis, NA.ORCID iD: 0000-0003-4950-6646
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2011 (English)In: Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics, 2011Conference paper, Published paper (Refereed)
Abstract [en]

The nervous system encompasses structure and phenomena at different spatial and temporal scales from molecule to behavior. In addition, different scales are described by different physical and mathematical formalisms. The dynamics of second messenger pathways can be formulated as stochastic reaction-diffusion systems [1] while the electrical dynamics of the neuronal membrane is often described by compartment models and the Hodgkin-Huxley formalism. In neuroscience, there is an increasing need and interest to study multi-scale phenomena where multiple scales and physical formalisms are covered by a single model. While there exists simulators/frameworks, such as GENESIS and MOOSE [3], which span such scales (kinetikit/HH-models), most software applications are specialized for a given domain. Here, we report about initial steps towards a framework for multi-scale modeling which builds on the concept of multi-simulations [2]. We aim to provide a standard API and communication framework allowing parallel simulators targeted at different scales and/or different physics to communicate on-line in a cluster environment. Specifically, we show prototype work on simulating the effect on receptor induced cascades on membrane excitability. Electrical properties of a compartment model is simulated in MOOSE, while receptor induced cascades are simulated in NeuroRD  [4,7] . In a prototype system, the two simulators are connected using PyMOOSE [5] and JPype [6]. The two models with their different physical properties (membrane currents in MOOSE, molecular biophysics in NeuroRD), are joined into a single model system.  We demonstrate the interaction of the numerical solvers of two simulators (MOOSE, NeuroRD) targeted at different spatiotemporal scales and different physics while solving a multi-scale problem. We analyze some of the problems that may arise in multi-scale multi-simulations and present requirements for a generic API for parallel solvers. This work represents initial steps towards a flexible modular framework for simulation of large-scale multi-scale multi-physics problems in neuroscience. References 1. Blackwell KT: An efficient stochastic diffusion algorithm for modeling second messengers in dendrites and spines. J Neurosci Meth 2006, 157: 142-153. 2. Djurfeldt M, Hjorth J, Eppler JM, Dudani N, Helias M, Potjans TC, Bhalla US, Diesmann M, Hellgren Kotaleski J, Ekeberg Ö: Run-Time Interoperability Between Neural Network Simulators Based on the MUSIC Framework. Neurinform 2010, 8: 43-60. 3. Dudani N, Ray S, George S, Bhalla US: Multiscale modeling and interoperability in MOOSE. Neuroscience 2009, 10(Suppl 1): 54. 4. Oliveira RF, Terrin A, Di Benedetto G, Cannon RC, Koh W, Kim M, Zaccolo M, Blacwell KT: The Role of Type 4 Phosphdiesterases in Generating Microdomains of cAMP: Large Scale Stochastic Simulations.

Place, publisher, year, edition, pages
2011.
Keyword [en]
Computational neuroscience, Digital atlasing
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-58754DOI: 10.3389/conf.fninf.2011.08.00074OAI: oai:DiVA.org:kth-58754DiVA: diva2:473940
Conference
4th INCF Congress of Neuroinformatics, Boston, USA, 4 Sep - 6 Sep, 2011
Note
QC 20120113Available from: 2012-01-08 Created: 2012-01-08 Last updated: 2012-01-13Bibliographically approved

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Hanke, MichaelHellgren Kotaleski, Jeanette

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Brandi, MayaTalukdar, Husain AhammadHanke, MichaelDjurfeldt, MikaelHellgren Kotaleski, Jeanette
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